Where SQACE fits
Verification, not just monitoring.
SQACE is complementary to DQ monitoring and goes beyond classical ETL testing.
In comparison
| SQACE | DQ monitoring | Classical ETL testing | |
|---|---|---|---|
| What is tested? | The processing | The data | The ETL route |
| Core question | Is the result correct? | Does the data look good? | Is the SQL correct? |
| Method | Rule-based expected-vs-actual | Anomaly detection, thresholds | SQL comparisons |
| Who uses it? | Business + IT | IT | IT |
| Migrations | Mapping verification, full volume | Not intended | Limited |
Already have DQ in your stack?
Platform checks watch the data. SQACE checks the result.
Modern data platforms ship their own data-quality features, such as expectations in Databricks or data metric functions in Snowflake, alongside DQ observability tools. They check rules and anomalies within one platform, and they are useful there.
But they do not answer one question: did the processing produce the functionally correct result? SQACE computes the expected result from your rule set as an independent second calculation and compares it against the actual output, across system and platform boundaries and migrations, independent of the stack and readable for the business side. This complements platform checks rather than replacing them.
How it fits
SQACE verifies, your toolchain orchestrates.
SQACE replaces none of your existing tools. It adds the missing verification layer: an instance outside the stack that the processing was built with.
How SQACE connects and runs securely (data sources, test management, CI/CD, operations): Security & architecture.